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Technical Report NC-TR-98-009
Learning via Internal Representation
(Extended Abstract)
Eli Dichterman
LSE & RHUL
UK
Keywords:
Internal representation; learning framework
Received:
11-MAY-1998
Abstract
We present a learning framework based on reducing a learning task
to the problem of finding a good internal representation of the input
examples; a good internal representation is a set of features, relative
to which a simple generalization rule, such as a linear hyperplane
classifier, can be applied to obtain a good approximation of the target.
Finding a good internal representation of a learning problem is especially
useful when the same representation is good for a set of similar tasks,
such as the recognition of several different characters. Although
the problem of extracting a set of informative features is not easier,
in general, than the learning problem itself, we show that some of
the most effective learning mechanisms, such as Boosting and Support
Vector Machine, are actually based on efficient methods of extracting
good internal representations of the input data. In particular, we
analyze and correct a recent approach based on approximately embedding
a distribution-depended learning task in a high-dimensional Euclidean
space.
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